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共 18085 条记录，以下是31-40 订阅

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DSSNet: a transformer-based network for dense scene text detection and recognition in complex environments 5

DSSNet: a transformer-based network for dense scene text det...

引用

5th international conference on signal processing and Computer Science, SPCS 2024

作者： Li, Jing Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

This paper presents a novel approach for dense scene text detection called DSSNet (Dense Script Spotter Network). The network leverages ResNet and FPN for feature extraction, employing multi-scale feature fusion and Transformer-based feature processing to enhance text recognition across varying sizes. The method generates text instance shapes using Bézier central curves and performs text recognition by integrating positional query information. Experimental results on the DSTD1500 and ICDAR2015 datasets demonstrate that DSSNet outperforms existing methods in terms of text localization accuracy, recognition accuracy, and annotation flexibility. © 2025 SPIE.

关键词： Feature extraction

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Shaft Rotation Monitoring Using Radar signal processing and Wavelet Transform 3rd

Shaft Rotation Monitoring Using Radar Signal Processing and ...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Valuyskiy, Denis Vityazev, Sergey Vityazev, Vladimir Ryazan State Radio Engineering University Gagarina. 59/1 Ryazan390005 Russia

ISBN: (纸本)9789819743582

This article discusses the problem of shaft rotation control for continuous testing of industrial equipment using a radar sensor. The FMCW radar with a frequency of 77 Hz is used to irradiate a rotating shaft and obtain information about the uniformity of its rotation. A test bench has been developed and radar reflections are recorded. Then the real signals are analyzed and an approach to signal processing and decision-making rules are proposed. The approach is based on wavelet transform and neural classification of rotating shifts with deep learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Radar signal processing

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Infrared and Visible Image Fusion with Hierarchical Human Perception

Infrared and Visible Image Fusion with Hierarchical Human Pe...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yang, Guang Li, Jie Liu, Xin Zhong, Zhusi Gao, Xinbo School of Electronic Engineering Xidian University Xi'an China Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350368741

Image fusion combines images from multiple domains into one image, containing complementary information from source domains. Existing methods take pixel intensity, texture and high-level vision task information as the standards to determine preservation of information, lacking enhancement for human perception. We introduce an image fusion method, Hierarchical Perception Fusion (HPFusion), which leverages Large vision-Language Model to incorporate hierarchical human semantic priors, preserving complementary information that satisfies human visual system. We propose multiple questions that humans focus on when viewing an image pair, and answers are generated via the Large vision-Language Model according to images. The texts of answers are encoded into the fusion network, and the optimization also aims to guide the human semantic distribution of the fused image more similarly to source images, exploring complementary information within the human perception domain. Extensive experiments demonstrate our HPFusoin can achieve high-quality fusion results both for information preservation and human visual enhancement. Our code is available at https://***/SSyangguang/HPFusion. © 2025 IEEE.

关键词： Human Perception Image Fusion Large vision-Language Model

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Enhancing Remote Sensing vision-Language Models for Zero-Shot Scene Classification

Enhancing Remote Sensing Vision-Language Models for Zero-Sho...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： El Khoury, Karim Zanella, Maxime Gérin, Benoît Godelaine, Tiffanie Macq, Benoît Mahmoudi, Saïd De Vleeschouwer, Christophe Ayed, Ismail Ben UCLouvain Belgium UMons Belgium ÉTS Montreal Canada

ISBN: (纸本)9798350368741

vision-Language Models for remote sensing have shown promising uses thanks to their extensive pretraining. However, their conventional usage in zero-shot scene classification methods still involves dividing large images into patches and making independent predictions, i.e., inductive inference, thereby limiting their effectiveness by ignoring valuable contextual information. Our approach tackles this issue by utilizing initial predictions based on text prompting and patch affinity relationships from the image encoder to enhance zero-shot capabilities through transductive inference, all without the need for supervision and at a minor computational cost. Experiments on 10 remote sensing datasets with state-of-the-art vision-Language Models demonstrate significant accuracy improvements over inductive zero-shot classification. Our source code is publicly available on Github: https://***/elkhouryk/RS-TransCLIP. © 2025 IEEE.

关键词： remote sensing scene classification transductive inference vision-language models zero-shot

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Semi-Automatic Labeling for Action Recognition by Diversity Preserving Sampling

Semi-Automatic Labeling for Action Recognition by Diversity ...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Ando, Ryuhei Shibata, Takashi Takahashi, Toru NEC Corporation Japan

ISBN: (纸本)9798350368741

Deep learning for action recognition is an important technology for understanding videos. However, collecting video training dataset for deep learning model with low cost while maintaining enough diversity is challenging. In this paper, we propose a semi-automatic labeling framework for action recognition by diversity-preserving sampling. The proposed framework utilizes a pre-trained vision-language model (VLM) to search through video clips to filter data that matches the text that describes the appropriate context for the target action. Since this simple approach by VLM tends to lack diversity, our framework is also equipped with diversity-preserving sampling that consists of two sampling strategies. One is confidence-based weighted sampling, which is based on action class confidence obtained from VLM, and the other is isolate-constraint-based weighted sampling, which samples points that are far apart in the text-image feature space. We conduct experiments to demonstrate that the proposed approach efficiently collects data with variations that could train a better action recognition model than the baseline. © 2025 IEEE.

关键词： action recognition labeling framework text-video retrieval vision-language model

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Hand Gesture Recognition Method based on mmWave Radar with MobileViT and Knowledge Distillation 16

Hand Gesture Recognition Method based on mmWave Radar with M...

引用

16th international conference on signal processing Systems, ICSPS 2024

作者： Zhang, Xiangqun Ge, Zhizhou Lu, Kai Du, Genyuan Shen, Jiawen Gao, Xiangqian School of Information Engineering Xuchang University Xuchang461000 China Henan International Joint Laboratory of Polarization Sensing and Intelligent Signal Processing Xuchang461000 China Henan Shengshi Hengxin Technology Co. Ltd Xuchang461111 China

ISBN: (纸本)9781510689251

Human-hand gesture recognition using millimetre wave radar is attractive in human-computer interfaces, industrial Internet of Things, and smart home. However, the existing CNN or RNN model is so complex and large that it is hard to apply to mobile vision tasks and embedded devices. This paper proposes dual lightweight convolutional neural networks, MobileViT and knowledge distillation, to solve this problem. Firstly, we acquire the original radar echoes of frequency-modulated continuous wave (FWCW) signal and reshape the three-dimensional matrix by Chirps * Samples * Frames. Then, we perform the signal processing method to eliminate the noise and static background. Secondly, we employ the Fast Fourier Transform to extract the hand gestures feature map of distance and Doppler information. Finally, the feature map is input into the modified MobileViT to recognize dynamic hand gestures, and knowledge distillation models is used to simplify the model structure. The experimental results show that the parameter space complexity of the constructed model is reduced to 0.018 M, and the computational complexity is 0.082 GFLOPs after knowledge distillation *** method is verified in 12 complex gesture. It has the potential to be applied in mobile tasks and embedded devices. © 2025 SPIE.

关键词： Fast Fourier transforms

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Portable System for processing sEMG signals with Neural Network Models for Measuring Hand Grip Force

Portable System for Processing sEMG Signals with Neural Netw...

引用

Joint international conference of the 14th international conference on Mechanisms and Mechanical Transmissions and 26th international conference on robotics, MTM and robotics 2024

作者： Cobzac, Corina-Ioana Avram, Mihai National University of Science and Technology POLITEHNICA Bucharest Splaiul Independenței 313 Bucharest060042 Romania

ISBN: (纸本)9783031875366

This paper contains the way of making a portable acquisition system of a sEMG signal from the extensor digitorum muscle with real-time processing of this signal to generate hand grip force information. The system represents a viable option for controlling actuators of a robotic hand or exoskeletons. The system is built with a Biometrics SX230FW sensor, a Raspberry Pi 4 single-board computer equipped with an MCC 118 data acquisition module and a custom-made hand grip force measurement device based on a load cell and the HX711 analog-to-digital converter. This study show cases the potential to transform muscle monitoring and rehabilitation with an accessible, portable, and efficient system built on advanced technologies. The real-time data utilization and seamless integration with other technologies pave the way for new research opportunities and practical applications across various fields, potentially leading to advancements in wearable devices, prosthetics, and human-robot interaction systems for enhanced mobility and rehabilitation outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Analog to digital conversion

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SelaFD:Seamless Adaptation of vision Transformer Fine-tuning for Radar-based Human Activity Recognition

SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wang, Yijun Wang, Yong Xu, Chendong Yao, Shuai Wu, Qisong Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

ISBN: (纸本)9798350368741

Human Activity Recognition (HAR) such as fall detection has become increasingly critical due to the aging population, necessitating effective monitoring systems to prevent serious injuries and fatalities associated with falls. This study focuses on fine-tuning the vision Transformer (ViT) model specifically for HAR using radar-based Time-Doppler signatures. Unlike traditional image datasets, these signals present unique challenges due to their non-visual nature and the high degree of similarity among various activities. Directly fine-tuning the ViT with all parameters proves suboptimal for this application. To address this challenge, we propose a novel approach that employs Low-Rank Adaptation (LoRA) fine-tuning in the weight space to facilitate knowledge transfer from pre-trained ViT models. Additionally, to extract fine-grained features, we enhance feature representation through the integration of a serial-parallel adapter in the feature space. Our innovative joint fine-tuning method, tailored for radar-based Time-Doppler signatures, significantly improves HAR accuracy, surpassing existing state-of-the-art methodologies in this domain. Our code is released at https://***/wangyijunlyy/SelaFD. © 2025 IEEE.

关键词： Fine-Tuning Human Activity Recognition Time-Doppler vision Transformer

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Leveraging Visual Captions for Enhanced Zero-Shot HOI Detection

Leveraging Visual Captions for Enhanced Zero-Shot HOI Detect...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zeng, Yanqing Mao, Yunyao Lu, Zhenbo Zhou, Wengang Li, Houqiang EEIS Department University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350368741

Zero-shot Human-Object Interaction (HOI) detection aims to identify both seen and unseen HOI categories in an image. Most existing methods rely on semantic knowledge distilled from CLIP to find novel interactions but fail to fully exploit the powerful generalization ability of vision-language models, leading to impaired transferability. In this paper, we introduce a novel framework for zero-shot HOI detection. We first utilize vision-language models (VLMs) to generate visual captions from multiple perspectives, including humans, objects, and environments, to enhance interaction understanding. Then, we propose a multi-modal fusion encoder to fully leverage these visual captions. Additionally, to equip the HOI detector with a thorough consideration of contextual information in the image, we design a novel multi-branch HOI network that aggregates features at the instance, union, and global levels. Experiments on prevalent benchmarks demonstrate that our model achieves promising performance under a variety of zero-shot settings. The source codes are available at https://***/aqingcv/VC-HOI. © 2025 IEEE.

关键词： Human-object Interaction Multimodal fusion vision-language Model Zero-shot

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Chitrarth: Bridging vision and Language for a Billion People

Chitrarth: Bridging Vision and Language for a Billion People

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Khan, Shaharukh Tarun, Ayush Ravi, Abhinav Faraz, Ali Pokala, Praveen Kumar Bhangare, Anagha Kolla, Raja Khatri, Chandra Agarwal, Shubham Krutrim AI Bangalore India

ISBN: (纸本)9798350368741

Recent multimodal foundation models are primarily trained on English or high resource European language data, which limits their applicability to other medium and low-resource languages, such as the Indian languages. To address this limitation, we introduce Chitrarth (Chitra: Image;Artha: Meaning), an inclusive vision-Language Model (VLM), specifically targeting the rich linguistic diversity and visual reasoning across 10 prominent Indian languages. Our model effectively integrates a state-of-the-art (SOTA) multilingual Large Language Model (LLM) with a vision module, primarily trained on multilingual image-text data. Furthermore, we also introduce BharatBench, a comprehensive framework for evaluating VLMs across various low resource languages, ultimately contributing to more diverse and effective AI systems. Our model presents SOTA results for benchmarks across Indian languages while retaining its efficiency in English. Through our research, we aim to set new benchmarks in multilingual-multimodal capabilities, offering substantial improvements over existing models and establishing a foundation for facilitating future advancements in this arena. © 2025 IEEE.

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